HybridSolarNet: A Lightweight and Explainable EfficientNet-CBAM Architecture for Real-Time Solar Panel Fault Detection

TL;DR

HybridSolarNet is a lightweight, explainable deep learning model combining EfficientNet-B0 and CBAM, achieving high accuracy and real-time performance for solar panel fault detection on UAVs, with enhanced interpretability.

Contribution

It introduces HybridSolarNet, a novel efficient and explainable model that outperforms baselines in accuracy and speed for solar fault detection, suitable for edge devices.

Findings

Achieved over 92% accuracy and 0.92 F1-score on Kaggle dataset.

Requires only 16.3 MB storage, enabling deployment on edge devices.

Runs at 54.9 FPS with GPU support, suitable for real-time UAV applications.

Abstract

Manual inspections for solar panel systems are a tedious, costly, and error-prone task, making it desirable for Unmanned Aerial Vehicle (UAV) based monitoring. Though deep learning models have excellent fault detection capabilities, almost all methods either are too large and heavy for edge computing devices or involve biased estimation of accuracy due to ineffective learning techniques. We propose a new solar panel fault detection model called HybridSolarNet. It integrates EfficientNet-B0 with Convolutional Block Attention Module (CBAM). We implemented it on the Kaggle Solar Panel Images competition dataset with a tight split-before-augmentation protocol. It avoids leakage in accuracy estimation. We introduced focal loss and cosine annealing. Ablation analysis validates that accuracy boosts due to added benefits from CBAM (+1.53%) and that there are benefits from recognition of classes with imbalanced samples via focal loss. Overall average accuracy on 5-fold stratified cross-validation experiments on the given competition dataset topped 92.37% +/- 0.41 and an F1-score of 0.9226 +/- 0.39 compared to baselines like VGG19, requiring merely 16.3 MB storage, i.e., 32 times less. Its inference speed measured at 54.9 FPS with GPU support makes it a successful candidate for real-time UAV implementation. Moreover, visualization obtained from Grad-CAM illustrates that HybridSolarNet focuses on actual locations instead of irrelevant ones.